The need for an accurate and non-invasive method for determining attributes of or analyte concentrations in bodily tissues, bodily fluids or other biological samples is well documented. For example, accurate non-invasive measurement of blood glucose levels in diabetic patients would greatly improve diabetes treatment. U.S. Pat. No. 5,379,764 to Barnes et al. discloses the necessity for diabetics to frequently monitor blood glucose levels. The more frequent the blood glucose levels are measured, the less likely the occurrence of large swings in blood glucose levels. These large swings are associated with the very undesirable short-term symptoms and long-term complications of diabetes. Such long-term complications include heart disease, arteriosclerosis, blindness, stroke, hypertension, kidney failure, and premature death.
Several systems have been proposed for the non-invasive measurement of blood glucose levels. However, despite these efforts, direct and invasive measurements (e.g., blood sampling by a lancet cut into the finger) are still necessary for most if not all presently FDA approved and commercially available glucose monitors. This is believed so compromising to the diabetic patient that frequent blood glucose measurement, which is necessary to ensure effective diabetes management, is rarely achieved.
The various proposed non-invasive methods for determining blood glucose level generally utilize quantitative infrared spectroscopy as a theoretical basis for analysis. In general, these methods involve probing glucose containing tissue using infrared radiation in transmission or in diffuse reflectance. It is known that glucose absorbs at multiple frequencies in both the mid- and near-infrared range. There are, however, other infrared active analytes in the tissue and blood that also absorb at similar frequencies. Due to the overlapping nature of these absorption bands, no single or specific frequency can be used for reliable non-invasive glucose measurement. Analysis of spectral data for glucose measurement thus requires evaluation of many spectral intensities over a wide spectral range to achieve the sensitivity, precision, accuracy, and reliability necessary for quantitative determination.
U.S. Pat. No. 4,975,581 to Robinson et al. discloses a method and apparatus for measuring a characteristic of unknown value in a biological sample using infrared spectroscopy in conjunction with a multivariate model that is empirically derived from a set of spectra of biological samples of known characteristic values. The above-mentioned characteristic is generally the concentration of an analyte, such as glucose, but also may be any chemical or physical property of the sample. The method of Robinson et al. involves a two-step process that includes both calibration and prediction steps.
In the calibration step, the infrared light is coupled to calibration samples of known characteristic values so that there is differential attenuation of at least several wavelengths of the infrared radiation as a function of the various components and analytes comprising the sample with known characteristic value. The infrared light is coupled to the sample by passing the light through the sample or by reflecting the light from the sample. Absorption of the infrared light by the sample causes intensity variations of the light that are a function of the wavelength of the light. The resulting intensity variations are measured for the set of calibration samples of known characteristic values. Original or transformed intensity variations are then empirically related to the known characteristic of the calibration samples using a multivariate algorithm to obtain a multivariate calibration model. The model preferably accounts for subject variability (both intra-subject and inter-subject), instrument variability and environment variability.
A further method of building a calibration model and using such model for prediction of analytes in or attributes of tissue is disclosed in commonly assigned U.S. Pat. No. 6,157,041 to Thomas et al., entitled “Method and Apparatus for Tailoring Spectrographic Calibration Models,” the disclosure of which is incorporated herein by reference.
In the prediction step, the infrared light is coupled to a sample of unknown characteristic value, and the calibration model is applied to the original or transformed intensity variations of the appropriate wavelengths of light measured from this unknown sample. The result of the prediction step is the estimated value of the characteristic of the unknown sample.
As mentioned above, the multivariate calibration model preferably accounts for instrument variability and environment variability. In addition, it is desirable that the model accounts for such variability over time. In other words, in the practical use of a multivariate calibration model, it is desirable that prediction errors or model applicability remain stable over time. It is known that prediction errors can be caused by changes in the measuring instrument or the measurement environment over time. See, for example, H. Swierenga, et. al., Applied Spectroscopy, Vol. 52, No. 1, 1998. As instruments change or drift over time, they produce variations in the spectra that reduce the ability of a calibration model to make accurate predictions. In order to maintain a multivariate calibration over time, the effect/magnitude of these variations must be reduced as much as possible.
One approach involves manipulating the calibration model itself. The most basic method is re-calibrating the instrument. In other words, when the multivariate model becomes invalid, due to a drift in the instrument response, the entire calibration procedure is repeated. This is a time and labor-intensive process, and, if the original calibration samples are unstable, a completely new set of samples must be prepared, which is not always practical. Because of the amount of effort involved in re-calibrations, this option is not a favorable one. Furthermore, for non-invasive in-vivo calibration models, there may be a complete lack of viable samples for re-calibration.
Another approach is to update the calibration model when the prediction samples begin to drift out of the calibration model space. This is especially useful in process monitoring, and can be achieved through the addition of new calibration samples that reflect changing analytical conditions. See, for example, Stork, Chris L.; Kowalski, Bruce R., Chemom. Intell. Lab. Syst. (1999), 48(2), 151-166; and Martens, H.; Westad, F.; Foulk, S.; Bernsten, H., Adv. Instrum. Control (1990), 45(Pt. 1), 371-381. This is less labor-intensive than an entire re-calibration since it only involves adding data from the new instrument state to the original calibration model data set. The problem with this approach is that it requires on-going data monitoring with a reliable method to evaluate when an update to the model is necessary. The method must also distinguish between an instrumental change that is normal and an instrumental change that indicates a problem that must be fixed (such as a component failure) so that the model is not simply updated with “bad” data. Thus, it can be difficult to ascertain at what point an update to the model becomes necessary while also establishing that the instrument itself is not failing.
Another approach for maintaining calibration is to build the calibration data set in such a way that expected instrumental variations are incorporated into the data. In other words, the calibration design should cover all relevant sources of variation that might be seen in future samples, so that future samples will not appear “unusual”. See, for example, Swierenga, H.; de Weijer, A. P.; van Wijk, R. J.; Buydnes, L. M. C., Chemom. Intell. Lab. Syst. (1999), 49(1), 1-17; De Noord, O. E., Chemom. Intell. Lab. Syst. (1994), 25, 85-97; and Thomas, E. V.; Ge, N., Technometics, (2000), 42(2), 168-176. Calibration samples must therefore be measured at different instrumental states and at different environmental conditions. A disadvantage of this approach is that it requires the burdensome task of measuring many more calibration samples to provide enough degrees of freedom to estimate the additional parameters. Another disadvantage is that it is often difficult to foresee all relevant variation sources. This creates the possibility that the instrument state may vary outside the model space despite the experimental design. When a variation occurs that is not accounted for in the model, the calibration will no longer be valid.
A related approach is to utilize an instrument-standardization technique for mapping the instrument in one state to an instrument in another state. See, De Noord, O. E., Chemom. Intell. Lab. Syst. (1994), 25, 85-97. This technique works best with a selection of “real” samples, which means that the transfer samples should ideally be a subset of those from the calibration set. However, it is often difficult to span the relevant data space with “generic” samples, and “real” calibration samples are often not reproducible and, therefore, impractical.
Yet another approach is to use mathematical pre-processing techniques to correct for the spectral variations caused by instrument drift over time. For example, such pre-processing methods include the use of first and second derivatives and other mathematical techniques to correct for constant and sloping baselines, and the use of Kalman filters to correct for drift. See, for example, Faber, N. M., Anal. Chem. (1999), 71(3), 557-565; Johansen, I. B.; Lines, G. T.; Honne, A.; Midtgaard, T., Appl. Spectrosc., (1997), 51(10), 1540-1546; and Rutan S C, Bouveresse E, Andrew K N, Worsfold P J, Massart D L, Chemometrics And Intelligent Laboratory Systems, (1996) 35(2) 199-211. Pre-processing methods inherently rely on assumptions about the instrument's spectral response. If the assumptions do not hold true, then the spectra will not be corrected sufficiently for the model to provide an accurate prediction. The further away the assumption is from reality, the more residual variation will remain in the spectrum and the more artifacts will be added to the spectrum. Such residual variations and artifacts seriously compromise the measurement of small concentrations of analyte, because even a small uncertainty in a large background signal creates a much larger uncertainty in the small analyte signal. Powell, J. R.; Wasacz, F. M.; Jakobsen, R. J., Appl. Spectrosc., (1986), 40(3), 339-344.
A variation of this approach is to calculate a background spectrum based on some assumptions, and then subtract that background spectrum from the sample spectrum. Because a static measure of the background will not compensate for background shifts due to instrument changes, different algorithms (statistical tests and heuristic spectral interpretation) may be used to estimate the background signal for subtraction from the sample response. See, for example, Salit, M. L.; Collins, J. B.; Yates, D. A., Appl. Spectrosc., (1994), 48(8), 915-925. However, as with the other pre-processing techniques described previously, this method also relies on assumptions that may not hold true and may introduce artifacts. Thus, as with all methods that involve estimators, this method is not sufficiently sensitive to estimate the background when the analyte signal is much smaller than the background signal.
As in some of the scientific literature discussed above, several of the patents discussed below disclose methods of dealing with changes in the baseline response of the instrument, where the goal is to isolate individual absorption peaks from the rest of the baseline instrument response. These techniques are applied to univariate spectral measurements, frequently in either plasma or fluorescence spectroscopy. However, none of these patents addresses the use of background measurements for the maintenance of a calibration model as in the present invention.
Franklin (U.S. Pat. No. 4,346,998) discusses measuring individual emission lines using plasma spectroscopy. Franklin describes a spectral background corrector system which causes wavelength scanning or modulation that allows specific absorption peaks to be identified and isolated. Again, Franklin does not offer a method for maintenance of multivariate calibration models over time as in the present invention.
U.S. Pat. No. 5,850,623 to Carman, Jr. et al. and European Patent Application No. 982 583 to Spragg discuss using standard “blank” samples to help reduce the effects of instrument changes. Carman discloses a method for standardizing Raman spectrometers using a reference sample for standardizing the optical instrument. Carman teaches that the choice of the reference sample is arbitrary, and Carman makes no mention of attempting to match the optical characteristics of the reference sample to the sample of interest. Carman suffers from the same limitations as the industry standard use of “blank” samples. Specifically, “blank” samples are spectrally dissimilar from the sample of interest being measured. In addition, “blank” samples or any other dissimilar background are not sufficiently sensitive to estimate the background when the analyte signal is much smaller than the background signal.
Spragg deals directly with attempting to measure the state of a scanning FTIR instrument for spectral correction. Spragg discloses a method of using PCA decomposition to reduce the amount of time required to obtain a useable background reference measurement. The background sample is described as being an empty sample holder. Spragg does not address the limitations of dissimilar backgrounds.
U.S. Pat. No. 5,830,133 to Osten et al. and U.S. Pat. No. 5,360,004 to Purdy et al. discuss mathematical data processing methods to deal with different types of measurement variance. Osten et al. deal with the effects of varying water pathlengths in the sample of interest by using a two compartment mathematical model to describe the sample. Purdy et al. describe the use of data preprocessing steps to reduce the effects of instrument variation by using derivatives of the spectral data. Neither Osten et al. nor Purdy et al. use a reference measurement, and both are inadequate for correcting for the types of instrument variation addressed by the present invention.
In summary, there is no generally accepted means of maintaining multivariate calibrations since none of the methods and theirs associated reference sample is a general solution to the problem. Moreover, when maintaining calibrations for samples where the analyte spectral absorption is much smaller than the gross sample spectrum, there is no known standard procedure to follow. In most other situations, it may be sufficient to use one of the techniques mentioned above, but when the analyte signal is very small, no known method is believed adequate, and spectral changes cannot simply be removed by an offset and/or slope correction. Subtle changes in the spectra must be accounted for in order to successfully maintain the calibration. None of the traditional methods does this, and predictive ability is, therefore, diminished with time.
To the extent that the methods described above use background samples, they do not use optically similar background samples to help maintain multivariate calibrations. An optically similar reference sample is a sample that optically interacts with the optical measurement system in a manner that simulates to a desired degree the optical interaction between the optical system and the test sample. One component of optical similarity is the creation of a spectral absorbance at selected wavelengths that is similar to the test sample. The result is similarly shaped spectra at these wavelengths for both the reference and measurement samples. To obtain a similar shape and matched average absorbance, the optically similar reference sample should absorb the same or similar intensity of light at each selected wavelength over the range of wavelengths measured. An optically dissimilar reference sample is a sample that optically interacts with the optical measurement system in a manner that does not adequately represent the instrument or environmental state. When a dissimilar reference is used, it generally consists of either air or the solvent in which the analyte of interest is dissolved (e.g., an empty sample holder). In cases where the spectral signature of the analyte of interest is large compared to the spectral features due to any other component in the system, the use of an empty sample holder may be sufficient to maintain a stable calibration model over time. Calibration in this instance is typically implemented by forming the ratio of the transmission spectrum of the unknown sample to the transmission spectrum of the reference sample. However, in cases where the spectral signature of the analyte of interest is much smaller than that of the other system components (e.g., glucose levels in blood or other aqueous solution), an empty sample holder or any other sample that is optically different from the prediction sample is not sufficient as a reference sample and is not effective for maintaining calibration.
There is a substantial need for devices and methods that maintain a stable multivariate calibration model designed for quantitative optical spectroscopic measurement of attributes or analytes in bodily tissue, blood or other biological samples. Such devices and methods are especially needed when the spectral absorbance of the attribute or analyte is small relative to the background.